Parisien Marc-André, Parks Sean A, Krawchuk Meg A, Little John M, Flannigan Mike D, Gowman Lynn M, Moritz Max A
Ecol Appl. 2014;24(6):1341-56. doi: 10.1890/13-1477.1.
Fire regimes of the Canadian boreal forest are driven by certain environmental factors that are highly variable from year to year (e.g., temperature, precipitation) and others that are relatively stable (e.g., land cover, topography). Studies examining the relative influence of these environmental drivers on fire activity suggest that models making explicit use of interannual variability appear to better capture years of climate extremes, whereas those using a temporal average of all available years highlight the importance of land-cover variables. It has been suggested that fire models built at different temporal resolutions may provide a complementary understanding of controls on fire regimes, but this claim has not been tested explicitly with parallel data and modeling approaches. We addressed this issue by building two models of area burned for the period 1980–2010 using 14 explanatory variables to describe ignitions, vegetation, climate, and topography. We built one model at an annual resolution, with climate and some land-cover variables being updated annually, and the other model using 31-year fire “climatology” based on averaged variables. Despite substantial differences in the variables' contributions to the two models, their predictions were broadly similar, which suggests coherence between the spatial patterns of annually varying climate extremes and long-term climate normals. Where the models' predictions diverged, discrepancies between the annual and averaged models could be attributed to specific explanatory variables. For instance, annually updating land cover allowed us to identify a possible negative feedback between flammable biomass and fire activity. These results show that building models at more than one temporal resolution affords a deeper understanding of controls on fire activity in boreal Canada than can be achieved by examining a single model. However, in terms of spatial predictions, the additional effort required to build annual models of fire activity may not always be warranted in this study area. From a management and policy standpoint, this key finding should boost confidence in models that incorporate climatic normals, thereby providing a stronger foundation on which to make decisions on adaptation and mitigation strategies for future fire activity.
加拿大北方森林的火灾状况受某些环境因素驱动，这些因素每年变化很大（如温度、降水），而其他因素相对稳定（如土地覆盖、地形）。研究这些环境驱动因素对火灾活动的相对影响表明，明确利用年际变化的模型似乎能更好地捕捉极端气候年份，而使用所有可用年份的时间平均值的模型则突出了土地覆盖变量的重要性。有人提出，以不同时间分辨率构建的火灾模型可能会提供对火灾状况控制的补充理解，但这一说法尚未通过并行数据和建模方法进行明确测试。我们通过构建两个1980 - 2010年烧毁面积模型来解决这个问题，使用14个解释变量来描述点火、植被、气候和地形。我们以年度分辨率构建了一个模型，气候和一些土地覆盖变量每年更新，另一个模型使用基于平均变量的31年火灾“气候学”。尽管两个模型中变量的贡献存在很大差异，但它们的预测大致相似，这表明每年变化的极端气候的空间模式与长期气候正常值之间具有一致性。在模型预测出现分歧的地方，年度模型和平均模型之间的差异可归因于特定的解释变量。例如，每年更新土地覆盖使我们能够识别可燃生物量与火灾活动之间可能存在的负反馈。这些结果表明，构建多个时间分辨率的模型比研究单个模型能更深入地理解加拿大北方森林火灾活动的控制因素。然而，就空间预测而言，在本研究区域构建年度火灾活动模型所需的额外努力可能并不总是合理的。从管理和政策角度来看，这一关键发现应增强对纳入气候正常值的模型的信心，从而为制定未来火灾活动的适应和缓解策略提供更坚实的决策基础。
